pass function arguments to both dplyr and ggplot

Question

I\'m confused about how to pass function argument into dplyr and ggplot codes. I\'m using the newest version of dplyr and ggplot2 Here is my code to produce a barplot (clari

Answer 1

Tidy evaluation is now fully supported in ggplot2 v3.0.0 so it's not necessary to use aes_ or aes_string anymore.

library(rlang)
library(tidyverse)

diamond_plot <- function (data, group, metric) {
    quo_group  <- sym(group)
    quo_metric <- sym(metric)

    data %>%
        group_by(!! quo_group) %>%
        summarise(price = mean(!! quo_metric)) %>%
        ggplot(aes(x = !! quo_group, y = !! quo_metric)) +
        geom_col()
}

diamond_plot(diamonds, "clarity", "price")

Created on 2018-04-16 by the reprex package (v0.2.0).

Answer 2

sinQueso's answer is promising but it misses the purpose of a function, which is to be adaptable to different data frames. The "price" variable is encoded in the function in the following line:

summarise(price=mean(!!quo_metric)) %>%

so this function will only work if the input variable is "price".

Here is a better solution that can be used for any data frame:

diamond_plot <- function (data, group, metric) {
        quo_group <- sym(group)
        quo_metric <- sym(metric)
        summary <- data %>%
                group_by(!!quo_group) %>%
                summarise(mean=mean(!!quo_metric))
                ggplot(summary, aes_string(x = group, y= "mean")) +
                geom_bar(stat='identity')
}
diamond_plot(diamonds, "clarity", "price")

Answer 3

The most "tidyeval" way to this problem to me looks as combination of quo_name and aes_string functions. Avoid using trailing underscore verbs like aes_ since they're getting deprecated.

diamond_plot <- function(data, group, metric) {
  quo_group <- enquo(group)
  str_group <- quo_name(quo_group)

  quo_metric <- enquo(metric)

  summary <- data %>%
     groupby(!!quo_group) %>%
     summarise(mean = mean(!!quo_metric))

  ggplot(summary) +
  geom_bar(aes_string(x = str_group, y = "mean"), stat = "identity")
}

diamond_plot(diamnonds, clarity, price)

Answer 4

I don't think you can that the "correct" way quite yet, as ggplot2 doesn't support the tidyeval syntax, but it's coming.

The best practice with the dplyr part of the code would be:

library(tidyverse)
library(rlang)

diamond_data <- function (data, group, metric) {
   quo_group <- enquo(group)
   quo_metric <- enquo(metric)
   data %>%
     group_by(!!quo_group) %>%
     summarise(price=mean(!!quo_metric))
}
diamond_data(diamonds, clarity, price)

To work around the lack of support of the tidyeval in ggplot2, you could do (note the quotes around the variables in the function call):

diamond_plot <- function (data, group, metric) {
    quo_group <- parse_quosure(group)
    quo_metric <- parse_quosure(metric)
    data %>%
        group_by(!!quo_group) %>%
        summarise(price=mean(!!quo_metric)) %>%
        ggplot(aes_(x = as.name(group), y=as.name(metric)))+
        geom_bar(stat='identity')
}
diamond_plot(diamonds, "clarity", "price")

EDIT -- Following @lionel's comment:

diamond_plot <- function (data, group, metric) {
    quo_group <- sym(group)
    quo_metric <- sym(metric)
    data %>%
        group_by(!!quo_group) %>%
        summarise(price=mean(!!quo_metric)) %>%
        ggplot(aes_(x = quo_group, y= quo_metric)) +
        geom_bar(stat='identity')
}
diamond_plot(diamonds, "clarity", "price")

Answer 5

You can go even further than Daniel's solution so that the name of the summary variable (metric) changes with the input.

diamond_plot <- function(data, group, metric) {
    quo_group <- rlang::sym(group)
    quo_metric <- rlang::sym(metric)
    metric_name <- rlang::sym(stringr::str_c("mean_", metric))
    data %>%
        group_by(!!quo_group) %>%
        summarize(!!metric_name := mean(!!quo_metric)) %>%
        ggplot(aes_(x = quo_group, y = metric_name)) +
        geom_bar(stat = 'identity')
}
diamond_plot(diamonds, "clarity", "price")